机器之心·2025-11-04 08:52Core Viewpoint - The article discusses the performance of six AI trading models during a turbulent market period in October 2025, highlighting their different strategies and outcomes in a real trading environment [2][9]. Group 1: AI Trading Experiment Overview - The AI-Trader project, led by Professor Huang Chao's team at the University of Hong Kong, began real trading tests amidst market volatility [3][4]. - The project received significant attention, garnering nearly 8,000 stars on GitHub within a week, indicating strong community interest in AI-driven trading technologies [4]. - Each of the six AI models started with $10,000 and operated independently in the Nasdaq 100 market, adhering to strict rules without external assistance [5][6]. Group 2: Performance of AI Models - The performance of the AI models varied significantly, with DeepSeek-Chat-V3.1 achieving the highest return of +13.89%, followed by MiniMax-M2 at +10.72% [7]. - In contrast, Gemini-2.5-Flash recorded a loss of -0.54%, illustrating the impact of trading strategies on performance [7]. - The Nasdaq 100 ETF (QQQ) only increased by +2.30% during the same period, highlighting the relative success of the AI models [7]. Group 3: Key Strategies and Insights - DeepSeek-Chat-V3.1 utilized a contrarian strategy, increasing positions in NVDA and MSFT during market panic, which proved effective with a return of +13.89% [14]. - MiniMax-M2 maintained a balanced portfolio with low turnover, resulting in a stable return of +10.72%, demonstrating the importance of consistency in high-volatility environments [15][16]. - Claude-3.7-Sonnet focused on long-term holdings, achieving a return of +7.12%, reflecting a classic value investment approach [17]. Group 4: Behavioral Finance Insights - The experiment served as a behavioral finance study, emphasizing the significance of trading discipline and market patience in achieving successful outcomes [10][11]. - The findings revealed that excessive trading and emotional decision-making can lead to poor performance, as seen with Gemini-2.5-Flash's high trading frequency and negative returns [22][24]. - The results suggest that effective investment decisions stem from managing uncertainty rather than attempting to predict market movements perfectly [31]. Group 5: Implications for AI in Finance - The success of the Chinese-developed models, DeepSeek and MiniMax, indicates a shift in AI capabilities from conversational skills to practical task execution in complex financial scenarios [32]. - The article posits that financial trading provides an ideal environment for validating AI decision-making capabilities, with potential applications extending to supply chain optimization and urban management [33]. - Future developments will require further validation in areas such as regulatory compliance and risk management to ensure stability in real-world applications [34].